1

Dr. Frederica Darema

Air Force Office of Scientific Research

The Power of:

Dynamic Data Driven Applications Systems (DDDAS)

…a Transformative Paradigm for advancedapplications/simulations and measurements

methodologies

June 3, 2011ICCS2011-DDDAS

-- InfoSymbioticSystems --

2

Component of a set ofTransformation Inducing

Directions• Multidisciplinary Research

Expanding Fundamental Research OpportunitiesUnification Paradigms – Multidisciplinary Thematic Areas InfoSymbiotic Systems

The Power of DDDAS – Dynamic Data Driven Applications Systems Multicore-based Systems

Unification of HEC w RT Data Acquisition & Control Systems Systems Engineering

Engineering Systems of Information (design-operation-maintenance-evolution)

Understanding the Brain and the MindFrom Cellular Networks to Human Networks

Network Systems Science (Network Science)Discover Foundational/Universal Principles across Networks

• The Aug2010 InfoSymbiotics/DDDAS Workshop -- multi-agency, jointly sponsored AFOSR-NSF, co-chaired by Profs: Craig Douglas, U-Wyo and Abani Patra, SUNY-Buffalo Report Possible Follow-up activities

3F. Darema

ExperimentMeasurements

Field-Data(on-line/archival)

User

Theory

(First Principles) Simulations

(Math.Modeling

Phenomenology

Observ’n Modeling

Design)

Dynamic Feedback & Control

Loop

Challenges:Application Simulations MethodsAlgorithmic Stability Measurement/Instrumentation MethodsComputing Systems Software Support

DDDAS: ability to dynamically incorporate additional data into an executing application, and in reverse, ability of an application to dynamically steer the measurement process

Software Architecture Frameworks Synergistic, Multidisciplinary

Research

Dynamic Integration of Computation & Measurements/Data(from the Real-Time to the High-End)

Unification of Computing Platforms & Sensors/Instruments

DDDAS – architect & adaptive mngmnt of sensor systems

Measurements ExperimentsField-Data

User

Theory

(First P

rincip

les)

OLD

(serialized and static)

Simula

tions

(Math

.Modelin

g

Phenomenolo

g

y)

a “revolutionary” concept enablingto design, build, manage and understand

complex systems

Dynamic Data Driven Applications Systems (DDDAS)

InfoSymbiotic Systems

Computational Science – t

he 3rd

paradigm

Data – the 4

th paradigm DDDAS /InfoSymbiotics

is the unifying

paradigm

44

LEAD: Users INTERACTING with Weather Infrastructure:

NSF Engineering Research Center for Collaborative Adaptive Sensing of the Atmosphere (CASA)

• Current (NEXRAD) Doppler weather radars are high-power and long range – Earth’s curvature prevents them from sensing a key region of the atmosphere: ground to 3 km

• CASA Concept: Inexpensive, dual-polarization phased array Doppler radars on cellular towers and buildings

– Easily view the lowest 3 km (most poorly observed region) of the atmosphere

– Radars collaborate with their neighbors and dynamically adapt the the changing weather, sensing multiple phenomena to simultaneously and optimally meet multiple end user needs

– End users (emergency managers, Weather Service, scientists) drive the system via policy mechanisms built into the optimal control functionality

NEXRADCASA

Slide courtesy Droegemeier

55

Experimental DynamicObservations

UsersADaM ADAS Tools

NWS National Static Observations & Grids

MesoscaleWeather

Local Observations

Local Physical Resources

Remote Physical (Grid) Resources

Virtual/Digital Resources and Services

LEAD: Users INTERACTING with Weather“The LEAD Goal Restated - to incorporate DDDAS “ - Droegemeier

Interaction Level II: Tools and People Driving Observing Systems – Dynamic Adaptation

“Sensor Networks & Computer Networks”Slide courtesy Droegemeier

66

Why A Service-Oriented Architecture?

• Flexible and malleable• Platform independence (emphasis on protocols, not platforms)

• Loose integration via modularity• Evolvable and re-usable (e.g. Java)• Interoperable by use of standards robustness

LEAD Service-Oriented Architecture

DistributedResources

ComputationSpecialized

ApplicationsSteerable

Instruments Storage

Data Bases

ResourceAccess Services GRAM

Grid FTP

SSH

Scheduler

LDM

OPenDAP GenericIngest Service

UserInterface

Desktop Applications• IDV• WRF Configuration GUI

LEAD Portal

PortletsVisualization Workflow Education

Monitor

Control

Ontology Query

Browse

Control

CrosscuttingServices

Authorization

Authentication

Monitoring

Notification

Con

figu

rati

on a

nd

E

xecu

tion

Ser

vice

s WorkflowMonitor

MyLEAD

WorkflowEngine/Factories

VO Catalog

THREDDS

Application ResourceBroker (Scheduler)

Host Environment

GPIR

Application Host

Execution Description

WRF, ADaM,IDV, ADAS

Application Description

Application & Configuration Services

Client Interface

Observations• Streams• Static• Archived

Dat

a S

ervi

ces

Wor

kfl

ow S

ervi

ces

Cat

alog

Ser

vice

s

RLSOGSA-

DAI

Geo-Reference GUI

ControlService

QueryService

StreamService

OntologyService

Decoder/ResolverService

Transcoder Service/ ESML

Slide courtesy Droegemeier

7

In 2000 NSF Workshop:Examples of Applications benefiting from the new

paradigm• Engineering (Design and Control)

– aircraft design, oil exploration(*), semiconductor mfg, structural eng– computing systems hardware and software design and runtime

(performance engineering and runtime compiler systems)• Crisis Management and Environmental Systems

– transportation systems (planning, accident response)– weather, hurricanes/tornadoes, floods, fire propagation

• Medical– Imaging, customized surgery, radiation treatment, etc– BioMechanics /BioEngineering

• Manufacturing/Business/Finance– Supply Chain (Production Planning and Control)– Financial Trading (Stock Mkt, Portfolio Analysis)

(*) Darema 1980 (& Gedanken Laboratory – Darema 1987, 1990)“DDDAS has the potential to revolutionize science, engineering, & management systems”

(Darema 2000)

“revolutionary” concept enabling to design, build, manage and understand complex systems

NSF/ENG Blue Ribbon Panel (Report 2006 – Tinsley Oden)“DDDAS … key concept in many of the objectives set in Technology Horizons”

Dr. Werner Dahm, (former) AF Chief Scientist (DDDAS Workshop, Aug 2010)

8F. Darema

DDDAS: Beyond Grid Computing “Extended Grid” – “SuperGRID”:

the Application Platform is the unified computational&measurement system

Measurement Grids

Applications

Com

puta

tion

al

Plat

form

s

Inst

rum

ents

Sens

ors

Cont

rolle

rs

Archi

val/

Stor

ed D

ata

Computational Grids

SuperGrids: Dynamically Coupled Networks of Data and Computations

9F. Darema

Fundamental Science & Technology Challenges in Enabling DDDAS

• Application modeling to support dynamic data inputs– interfacing applications with measurement systems– dynamically select appropriate application components

• multi-modal, multi-scale – dynamically invoke multiple scales/modalities

– switching to different algorithms/components depending on streamed data dynamic hierarchical decomposition (compt’n-sensor) and partitioning

• Algorithms– tolerant to perturbations of dynamic input data– handling data uncertainties, uncertainty propagation,

quantification

• Measurements– Multiple modalities, space/time distributed, data management

• Systems supporting such dynamic environments– dynamic execution support on heterogeneous environments

• Need new fundamental compiler advances (runtime-compiler)

– extended Spectrum of platforms: Grids of Sensor Networks and Computational platforms

– architect and manage heterogeneous/distributed sensor networks

– GRID Computing, and Beyond (SuperGrids)!!! - DDDAS environments are the most important manifestation of Grids/SuperGrids!

10F. Darema

Examples of Areas of DDDAS Impact

• Physical, Chemical, Biological, Engineering Systems– Chemical pollution transport (atmosphere, aquatic, subsurface), ecological

systems,molecular bionetworks, protein folding..• Medical and Health Systems

– MRI imaging, cancer treatment, seizure control• Environmental (prevention, mitigation, and response)

– Earthquakes, hurricanes, tornados, wildfires, floods, landslides, tsunamis, terrorist attacks

• Critical Infrastructure systems– Electric power systems, water supply systems, transportation networks and vehicles

(air, ground, underwater, space);

condition monitoring, prevention, mitigation of adverse effects, …• Homeland Security, Communications, Manufacturing

– Terrorist attacks, emergency response; Mfg planning and control • Dynamic Adaptive Systems-Software

– Robust and Dependable Large-Scale systems– Large-Scale Computational Environments

List of Projects/Papers/Workshops in www.cise.nsf.gov/dddas, www.dddas.org

+…• ICCS/DDDAS Workshop Series

yearly 2003 – todate• other workshops – community

organized

• 2 Workshops&Reports in 2000 and in 2006,

in www.cise.nsf.gov/dddas & www.dddas.org• August 30&31, 2010 Joint

AFOSR-NSF Workshop (multiple agencies participation)

* www.dddas.org (maintained by Prof. Craig Douglas)

11

(2000 -Through NGS/ITR Program)Pingali, Adaptive Software for Field-Driven Simulations

(2001 -Through ITR Program)• Biegler – Real-Time Optimization for Data Assimilation and Control of

Large Scale Dynamic Simulations• Car – Novel Scalable Simulation Techniques for Chemistry, Materials

Science and Biology• Knight – Data Driven design Optimization in Engineering Using

Concurrent Integrated Experiment and Simulation• Lonsdale – The Low Frequency Array (LOFAR) – A Digital Radio

Telescope• McLaughlin – An Ensemble Approach for Data Assimilation in the Earth

Sciences• Patrikalakis – Poseidon – Rapid Real-Time Interdisciplinary Ocean

Forecasting: Adaptive Sampling and Adaptive Modeling in a Distributed Environment

• Pierrehumbert- Flexible Environments for Grand-Challenge Climate Simulation

• Wheeler- Data Intense Challenge: The Instrumented Oil Field of the Future

(2002 -Through ITR Program)• Carmichael – Development of a general Computational Framework for the

Optimal Integration of Atmospheric Chemical Transport Models and Measurements Using Adjoints

• Douglas-Ewing-Johnson – Predictive Contaminant Tracking Using Dynamic Data Driven Application Simulation (DDDAS) Techniques

• Evans – A Framework for Environment-Aware Massively Distributed Computing

• Farhat – A Data Driven Environment for Multi-physics Applications • Guibas – Representations and Algorithms for Deformable Objects• Karniadakis – Generalized Polynomial Chaos: Parallel Algorithms for Modeling

and Propagating Uncertainty in Physical and Biological Systems • Oden – Computational Infrastructure for Reliable Computer Simulations• Trafalis – A Real Time Mining of Integrated Weather Data

(2003 -Through ITR Program)• Baden – Asynchronous Execution for Scalable Simulation in Cell Physiology• Chaturvedi– Synthetic Environment for Continuous Experimentation (Crisis

Management Applications)• Droegemeier-Linked Environments for Atmospheric Discovery (LEAD)• Kumar – Data Mining and Exploration Middleware for Grid and Distributed

Computing• Machiraju – A Framework for Discovery, Exploration and Analysis of

Evolutionary Data (DEAS)• Mandel – DDDAS: Data Dynamic Simulation for Disaster Management (Fire

Propagation)• Metaxas- Stochastic Multicue Tracking of Objects with Many Degrees of

Freedom• Sameh – Building Structural Integrity • {Sensors Program: Seltzer – Hourglass: An Infrastructure for Sensor

Networks}(2004 -Through ITR Program)

• Brogan – Simulation Transformation for Dynamic, Data-Driven Application Systems (DDDAS)

• Baldridge – A Novel Grid Architecture Integrating Real-Time Data and Intervention During Image Guided Therapy

• Floudas-In Silico De Novo Protein Design: A Dynamically Data Driven, (DDDAS), Computational and Experimental Framework

• Grimshaw: Dependable Grids• Laidlaw: Computational simulation, modeling, and visualization for understanding

unsteady bioflows• Metaxas – DDDAS - Advances in recognition and interpretation of human motion: An

Integrated Approach to ASL Recognition• Wheeler: Data Driven Simulation of the Subsurface: Optimization and Uncertainty

Estimation

Ghattas - MIPS: A Real-Time Measurement-Inversion-Prediction-Steering Framework for Hazardous EventsHow - Coordinated Control of Multiple Mobile Observing Platforms for Weather Forecast ImprovementBernstein – Targeted Data Assimilation for Disturbance-Driven Systems: Space weather ForecastingMcLaughlin - Data Assimilation by Field AlignmentLeiserson - Planet-in-a-Bottle: A Numerical Fluid-Laboratory Chryssostomidis - Multiscale Data-Driven POD-Based Prediction of the OceanNtaimo - Dynamic Data Driven Integrated Simulation and Stochastic Optimization for Wildland Fire ContainmentAllen - DynaCode: A General DDDAS Framework with Coast and Environment Modeling ApplicationsDouglas - Adaptive Data-Driven Sensor Configuration, Modeling, and Deployment for Oil, Chemical, and Biological Contamination near Coastal Facilities

• Clark - Dynamic Sensor Networks - Enabling the Measurement, Modeling, and Prediction of Biophysical Change in a Landscape

• Golubchik - A Generic Multi-scale Modeling Framework for Reactive Observing Systems

• Williams - Real-Time Astronomy with a Rapid-Response Telescope Grid

• Gilbert - Optimizing Signal and Image Processing in a Dynamic, Data-Driven Application System

• Liang - SEP: Intergrating Multipath Measurements with Site Specific RF Propagation Simulations

• Chen - SEP: Optimal interlaced distributed control and distributed measurement with networked mobile actuators and sensors

• Oden - Dynamic Data-Driven System for Laser Treatment of Cancer• Rabitz - Development of a closed-loop identification machine for

bionetworks (CLIMB) and its application to nucleotide metabolism• Fortes - Dynamic Data-Driven Brain-Machine Interfaces • McCalley - Auto-Steered Information-Decision Processes for

Electric System Asset Management• Downar - Autonomic Interconnected Systems: The National Energy

Infrastructure• Sauer- Data-Driven Power System Operations • Ball - Dynamic Real-Time Order Promising and Fulfillment for

Global Make-to-Order Supply Chains• Thiele – Robustness and Performance in Data-Driven Revenue

Management• Son - Dynamically-Integrated Production Planning and Operational

Control for the Distributed Enterprise

+…* projects, funded through other sources and “retargeted by the researchers to incorporate DDDAS”

* ICCS/DDDAS Workshop Series, yearly 2003 – todate• other workshops organized by the

community…

• 2 Workshop Reports in 2000 and in 2006, in www.cise.nsf.gov/dddas & www.dddas.org

* www.dddas.org (maintained by Prof. Craig Douglas)

(2005 DDDAS Multi-Agency Program - NSF/NIH/NOAA/AFOSR)

(1998- … precursor Next Generation Software Program) SystemsSoftware – Runtime Compiler – Dynamic Composition – Performance Engineering

12

Atlantic Dr.

Fifth St.

W. P

eachtree St.

Sp

ring St.

Stochastic maintenance &

inspection model

Long-term deterioration

models

Short-term deterioration models

Current loading & 1-day forecast

Short term forecast

Long-term forecast

2-20 years 1-10 years 1 week-2 years Minutes - 1 week Time frame

Layer 3: Data communication and integration

Layer 4: Data processing and transformation

Short-term facility plans

Long-term facility plans Maintenance

schedule Desired maintenance

& inspection frequency

Facility planning

Short term maintenance

planning

Operational decision making

Layer 5: Simulation & decision

Information valuation

Layer 2:

Condition sensors

Condition Histories

Iowa Sub 1

Condition Histories

Iowa Sub 2

Condition Histories

Iowa Sub 3

Condition Histories

Iowa Sub N

…. Condition Histories

ISU Sub1,2,3

Iowa/ISU Power System Model

Areva Simulator

(DTS)

Operating histories

Operational policies

Maintenance schedules

Facility R&R plans Areva EMS

Event selector Layer 1: The power system

Long term maintenance

planning

Probabilistic failure indices

Data Integration

Maintenance histories

Nameplate data

Decision implementation

Sensor deployment

BasicAlgorithms &

NumericalMethods

PipelineFlows

Biosphere/Geosphere

Neural Networks

Condensed MatterElectronic Structure

CloudPhysics

-

ChemicalReactors

CVD

PetroleumReservoirs

MolecularModeling

BiomolecularDynamics / Protein Folding

RationalDrug DesignNanotechnology

FractureMechanics

ChemicalDynamics Atomic

ScatteringsElectronicStructure

Flows in Porous Media

FluidDynamics

Reaction-Diffusion

MultiphaseFlow

Weather and ClimateStructural Mechanics

Seismic Processing

AerodynamicsGeophysical Fluids

QuantumChemistry

ActinideChemistry

CosmologyAstrophysics

VLSIDesign

ManufacturingSystems

MilitaryLogistics

NeutronTransport

NuclearStructure

QuantumChromo -Dynamics Virtual

Reality

VirtualPrototypes

ComputationalSteering

Scientific Visualization

MultimediaCollaborationTools

CAD

GenomeProcessing

Databases

Large-scaleData Mining

IntelligentAgents

IntelligentSearch

Cryptography

Number Theory

Ecosystems

EconomicsModels

Astrophysics

SignalProcessing

Data Assimilation

Diffraction & InversionProblems

MRI Imaging

DistributionNetworks

Electrical Grids

Phylogenetic TreesCrystallography

TomographicReconstruction

ChemicalReactors

PlasmaProcessing

Radiation

MultibodyDynamics

Air TrafficControl

PopulationGenetics

TransportationSystems

Economics

ComputerVision

AutomatedDeduction

ComputerAlgebra

OrbitalMechanics

Electromagnetics

Magnet DesignSource: Rick Stevens, Argonne National Lab and The University of Chicago

SymbolicProcessing

Pattern Matching

RasterGraphics

MonteCarlo

DiscreteEvents

N-Body

FourierMethods

GraphTheoretic

Transport

Partial Diff. EQs.

Ordinary Diff. EQs.

Fields

Be StaionGrid

8

7

6

0.6 0.7 0.8

FAULT

13

Why Now is Timely for increasing efforts

Several agencies reports - over the ~recent & over the last 2 years:

• The 2005 NSF Blue Ribbon Panel headed by Prof. Tinsley Oden– characterized DDDAS as visionary and revolutionary concept.

• The 2006 Workshop on DDDAS convened in 2006 – produced a report with comprehensive scope – covers scientific and technical advances needed to enable DDDAS capabilities– presents progress and wealth of examples where DDDAS has started making

impact

• the “14 Grand Challenges” posed by the National Academies of Engineering

• A 2007 CF21 NSF Report and several more recent TaskForces -> NSF CyberInfrastructure for the 21st Century (CIF21-NSF 2010) lays out – “a revolutionary new approach to scientific discovery in which advanced

computational facilities (e.g., data systems, computing hardware, high speed networks) and instruments (e.g., telescopes, sensor networks, sequencers) are coupled to the development of quantifiable models, algorithms, software and other tools and services to provide unique insights into complex problems in science and engineering.”

• Set of recent TaskForces by NSF have reported-back with recommendations reinforcing the need for this thrust (DDDAS)

• The 2010 AirForce Technology Horizons 2010 Report (Dr. Werner Dahm -AF ChiefSci)

– A Vision for transforming the Air Force in the next 20 years

14

The AirForce 10yr + 10 Yr Outlook: Technology Horizons Report

• Autonomous systems

• Autonomous reasoning and learning

• Resilient autonomy

• Complex adaptive systems

• V&V for complex adaptive systems

• Collaborative/cooperative control

• Autonomous mission planning

• Cold-atom INS

• Chip-scale atomic clocks

• Ad hoc networks

• Polymorphic networks

• Agile networks

• Laser communications

• Frequency-agile RF systems

• Spectral mutability

• Dynamic spectrum access

• Quantum key distribution

• Multi-scale simulation technologies

• Coupled multi-physics simulations

• Embedded diagnostics

• Decision support tools

• Automated software generation

• Sensor-based processing

• Behavior prediction and anticipation

• Cognitive modeling

• Cognitive performance augmentation

• Human-machine interfaces

http://www.af.mil/shared/media/document/AFD-100727-053.pdf

Werner Dahm: DDDAS … key concept in many of the objectives set in Technology Horizons

15

And some additional/starting opportunitiesOptimized Aircraft Routing – Cognizant of Weather

Predicting spread of volcanic ash – Mitigating constraints/damage from event

16

InfoSymbiotics/DDDASWhy Now More than Ever

• Scale/Complexity of Natural, Engineered and Societal Systems– the increasing complexity – and emphasis in complex systems

multi-scale/multi-modal modeling and algorithmic methods• Ubiquitous sensoring

– networks of large collections of heterogeneous sensors and actuators; other complex instruments • dynamically manage such sets of resources in optimized

ways through adaptive control executing application-model• improve over traditional static/ad-hoc ways of resource

management • Big Data

– “swimming in sensors and drowning in data” (Lt Gen Deptula)• Transformational Computational and Networking

Capabilities – increased in networking capabilities for streaming large data

volumes remotely– emerging multicore-based transformational computational

capabilities at the high-end and in real-time data acquisition and control systems• unprecedented computational capabilities• multicores across - simplify one of the dimensions in the

challenge of unification of high-end with real-time data-acquisition and control

17

Multicore-based Systems

Multicores in High-End Platforms• Multiple levels of hierarchies of processing nodes, memories, interconnects, latencies

Multicores in “measurement/data” Systems• Instruments, Controllers, Networks, Storage

SuperGrids: Dynamically Coupled Networks of Data and Computations

MPP NOW

SAR

tac-com

database

firecntl

firecntl

alg accelerator

database

SP

….

Mult

iple

le

vels

of

muti

core

s

Integrated/Unified Application Platforms

Multidisciplinary Research and Technology Development for:Adaptable Computing Systems Infrastructure

from the high-end to real-time data-acquisition & control systems

supporting applications adaptive mapping and optimized runtime

heterogeneous multilevel distributed parallelism system architectures – software architectures

SuperGRIDS

18

Emerging Computational Platforms

Landscape on Multicores:• multicore processing unit (MPU) – system on a chip (SoC)

– Heterogeneity within the multicore processing unit • each MPU will most likely include scalar, vector, GPU, and

FPGA• role of memristors in the evolution of MPUs, in particular

FPGAs– interconnections among MPUs

• “printed on the chip”, optical• multistage, combining networks, …new multidimensional

network architectures• multicores in high-end and mid-range platforms

– globally-distributed, meta-computing, heterogeneous, assemblies of networked workstations, networked supercomputing clusters, networked and adaptive platforms, together with their associated peripherals such as storage and visualization systems e.g. Grids, Clouds (Clouds are not sufficient solutions!)

– Hexascale range – multi-level hierarchies in processors, memories, networks, mem & nets architectures [“grids-in-a-box” (Paul Messina) - GiBs (Darema)]

• multicores in sensors, instrumentation, data acquisition, control systems …& integrated with mid/high-end unified platforms

Challenges and Opportunities through such systems

Reminder: There are good lessons

from the past not to forget

as we move into the future

19

Need for New Programming Environments

and Runtime support technologies(FD – 1997; NGS-1998)

• Emerging and future hardware platforms will have – multiple levels of processors, and multiple levels of interconnecting

networks – multiple levels of memory hierarchies (cache, main memory and

storage levels) – with multiple levels of bandwidths (variable at inter-node and intra-

node levels) and latencies (differing for different links, differing based on traffic)

–

• The questions range from: – how to express parallelism and optimally map and execute

applications on such platforms – how to enable load balancing, in the presence of multiple levels of the

platform architecture, and at what level (or levels) is load balancing applied

• becomes evident that static parallelization approaches are inadequate– one needs programming models that allow expressing parallelism so

that it can be exploited dynamically– for load balancing and hiding latency– for expressing dynamic flow control and synchronization possibly at

multiple levels• Need new systems software approaches (articulated in programs

I started) – new programming models (in ~’97-’98 I explicitly said, “let’s go

beyond SPMD”)– dynamic runtime compiler– dynamic application composition (application analytics, UQ)– “systems engineering”

exascale domain: prospects of addressing billion-way concurrency, and in the presence of heterogeneity of processing units

20

DynamicallyLink

&Execute

Dynamic Runtime Support needed for DDDAS environments Runtime Compiling System (RCS) and Dynamic Application Composition

ApplicationModel

Application Program

ApplicationIntermediate

Representation

CompilerFront-End

CompilerBack-End Performance

Measuremetns&

Models

DistributedProgramming

Model

ApplicationComponents

&Frameworks

Dynamic AnalysisSituation

LaunchApplication (s)

Interacting with Data Systems(archival data and on-line instruments)

Distributed Platform

Ada

ptab

leco

mpu

ting

Syst

ems

Infr

astr

uctu

re

Distributed Computing Resources

MPP NOW

SAR

tac-com

database

firecntl

firecntl

alg accelerator

database

SP

….

F. Darema

21

Systems Engineering(Engineering Systems of Information)

• System-level methods to design, build, and manage the operation, maintenance, extensibility, and interoperability of complex systems

in ways where the systems’ performance, fault-tolerance, adaptability, interoperability and extensibility is considered throughout this cycle

(limited component level, design level approaches – not sufficient!)

• Such complex systems include (examples): – heterogeneous and distributed sensor networks

– large platforms & other complex instrumentation systems & collections thereof

which need to satisfy/exhibit:

– adaptability and fault tolerance under evolving internal and external conditions

– extensibility/interoperability with other systems in dynamic and adaptive ways

• Systems engineering requires novel methods that can:– model, monitor, & analyze all components of such systems

– at multiple levels of abstraction

– individually and composed as a system architectural framework

New Directions: Systems Level Modeling and Analysis <–> Performance FrameworksPerformance Models & Resource Monitoring <->Operation Cycle, System Evolution

22

InfoSymbioticSystems/DDDAS Multidisciplinary Research

• applications modeling

• mathematical and statistical algorithms

• systems software

and

• measurement (instrumentation and control) systems

23F. Darema

Perform

ance

Engineering

Dynamic

Compilers

&

Application

Composition

Dynamic Data-Driven

Applicatio

n Systems

--

Symbiotic

Measurement&Simulatio

n

Systems

Enabling DDDAS

Systems Software

(NGS: 1998-2004)

(CSR/AES&SMA: 2004-to

date)

MultidisciplinaryResearch

Applications

Modeling

& Measurements

CSResearch

Multidisciplinary Researchin

applications modelingmathematical and statistical algorithms

measurement methodsdynamic, heterogeneous systems support

24

Where we are … & QUO VADIMUS

• DDDAS/InfoSymbiotics– high pay-off in terms of new capabilities– need fundamental and novel advances in several disciplines– well-articulated and well-structured research agenda from the

outset

• Progress has been made – it’s a “multiple S-curves” process– experience/advances cumulate to accelerating the pace of

progress in the future– we have started to climb the upwards slope of each of these

S-curves – need the trust of sustained, concerted, synergistic support

• Recent Workshop and Report (August 30&31, 2010)– DDDAS/InfoSymbiotics broad impact - Multi-agency interest – can capitalize on past/present progress through projects

started– timely in the landscape of: ubiquitous

sensoring/instrumentation, big-data, multicore-based high-performance systems, multiscale/multimodal modeling, uncertainty quantification, …

– the present landscape enriches the research agenda and opportunities

Applications ModelingMath&Stat Algorithms

Systems SoftwareInstrumentation/Control

Systems

25

Report of theAugust 2010 Multi-Agency Workshop on

InfoSymbiotics/DDDASThe Power of Dynamic Data Driven Applications Systems

Report OutlineExecutive Summary1. Introduction - InfoSymbiotics/DDDAS Systems 2. InfoSymbioticSystems/DDDAS Multidisciplinary Research3. Timeliness for Fostering InfoSymbiotics/DDDAS Research

3.1 Scale/Complexity of Natural, Engineered and Societal Systems3.2 Applications’ Modeling and Algorithmic Advances 3.3 Ubiquitous Sensors 3.4 Transformational Computational and Networking Capabilities

4. InfoSymbiotics/DDDAS and National/International Challenges 5. Science and Technology Challenges discussed in the Workshop

5.1 Algorithms, Uncertainty Quantification, Multiscale Modeling5.2 Large, Complex, and Streaming Data5.3 Autonomic Runtime Support in InfoSymbiotics/DDDAS5.4 InfoSymbioticSystems/DDDAS CyberInfrastructure Testbeds 5.5 InfoSymbioticSystems/DDDAS CyberInfrastructure Software Frameworks

6. Learning and Workforce Development 7. Multi-Sector, Multi-Agency Co-operation8. SummaryAppendices

Appendix-0 Workshop AgendaAppendix-1 Plenary Speakers BiosAppendix-2 List of Registered ParticipantsAppendix-3 Working Groups Charges

Report posted on www.dddas.org & AFOSR, NSF websites

26

Follow-up Activities

• DDDAS included in the new AFOSR BAA

• Discussions with NSF and other agencies for joint efforts

• The Aug2010 InfoSymbiotics/DDDAS workshop

included representation by several agencies:

DOD(AFOSR&AFRL, ONR&NRL, NRO&NRL)

NSF (MPS, OCI, ENG, CISE, …)

NASA

NIH

DTRA

DOE(Labs)

(other agencies have expressed interest)

Industry participation & interest

… more updates forthcoming….